Resource Management for Cognitive IoT Systems With RF Energy Harvesting in Smart Cities

Abstract

The number of Internet of Things (IoT) nodes is increasing in modern cities which demands spectrum and energy efficiency. Fifth-generation (5G) networks are considered as a key paradigm for the realization of future IoT applications. Particularly, cognitive radio and non-orthogonal multiple access are candidate technologies for 5G networks that can improve spectral efficiency and accommodate a large number of IoT devices. Furthermore, radio frequency (RF) energy harvesting can increase the energy efficiency of IoT networks. In this paper, we propose a resource management scheme for cognitive IoT network with RF energy harvesting in 5G networks. The objective is to maximize the throughput while assuring quality-of-service requirements in terms of data rate and minimum residual energy constraint on each IoT node. We use mixed integer linear programming and greedy approaches to solve the optimization problem. We then present the simulation results of the proposed scheme to exhibit the significant positive impact on the performance of the IoT network.